Your final project post should include:
A brief recap of your data, goals, and tasks, focusing on those that most directly influence your design:
Screenshots of and/or a link to your visualization implementation:
A summary of the key elements of your design and accompanying justification:
A discussion of your final evaluation approach, including the procedure, people recruited, and results:
A synthesis of your findings, including what elements of your approach worked well and what elements you would refine in future iterations.
Locate a dataset that you are interested in working with. The data should be sufficiently complex that you can ask lots of questions about it and engage in creative design techniques, but not so complex that you need specialized hardware or algorithmic approaches to analyze. While you are welcome to use any data you’d like, I recommend that your datasets are tabular (e.g., CSV, TSV, SQL, etc.), contain 5,000 or fewer datapoints (on the order of one hundred or so tends to be sufficiently interesting without causing lag in Altair), and is data that you’re comfortable discussing as part of the course (e.g., avoid data that is overly private or classified).
Discuss your dataset, including the data’s source, key attributes/dimensions of the data, and your goals for working with that data (i.e., what are the key questions you want to answer). Identify existing relevant visualizations for working with that data (either using the same data, showing the same concepts, or just that might provide some inspiration) and critique those visualizations based on the practices from this module. What works well? What might need improvement or to change to answer your target questions?
The dataset used is from the Union of Concerned Scientists (UCSUSA) which details all openly-known satellites orbitting the earth at the time of previous update - January 1, 2023.
name, country of owner, purpose, orbital information, launch information, etc..
Exported this notebook to html using 'jupyter nbconvert Active-Satellites-Notebook.ipynb --no-input --to html'
UCSUSA has a visualization highlighting each country on an image of a map that has satellites or not. They also distinguish between countries that launch satellites or not as well as have a slider depicting the same information from either 1966 or 2020. You can see it at the website above.
Pros:
Cons:
Overall, I believe it to be a successful visualization but has limited uses as it doesn't answer more interesting questions that live within this dataset. Giving the user the ability to reach more information depth through other methods of interaction would help improve this execution.
Your Module 1 discussion post identified some high-level goals for working with a dataset of interest to you. In this post, you will expand on those goals to characterize your target problem and develop some low-fidelity prototypes for working with that data. First, identify two to three tasks you would wish to complete with your data, identifying:
Why is a task pursued? (goal)
How is a task conducted? (means)
What does a task seek to learn about the data? (characteristics)
Where does the task operate? (target data)
When is the task performed? (workflow)
Who is executing the task? (roles)
Then, sketch a set of preliminary low-fidelity prototypes for addressing these tasks with the given data. You may either sketch freeform or use the Five Design Sheets approach to generate these prototypes (hand-sketched on paper is fine). Upload a copy of your sketches as part of your post.
Using the information gained by the high-level exploratory data analysis performed in part 1, the examples found from the data source, and preliminary sketching done for possible tasks we will now flesh out the tasks for this data.
Task 1: Data overview displaying key features organized by country.
Task 2: Using dates of launch and life expectancy, display age statistics across features
Task 3: Calculate and visualize orbital paths of active satellites.